Developing clinical algorithm for identifying acute lumbar spondylolysis in elementary school children – Classification and regression tree analysis

Abstract

ABSTRACT Objectives To develop a clinical algorithm for classifying acute lumbar spondylolysis from nonspecific low back pain in elementary school-aged patients using the classification and regression tree analysis. Methods Medical records of 73 school-aged patients diagnosed with acute lumbar spondylolysis or nonspecific low back pain were retrospectively reviewed. Fifty-eight patients were examined for establishing an algorithm and 15 were employed for testing its performance. The following data were retrieved: age, gender, school grades, days after symptom onset, history of low back pain, days of past low back pain, height, weight, body mass index, passive straight leg raise test results, hours per week spent on sports activities, existence of spina bifida, lumbar lordosis angle, and lumbosacral joint angle. Classification and regression tree analyses were performed 150 times using the bootstrap and aggregating method. Then, the results were integrated by majority vote, establishing an algorithm. Results Lumbar lordosis angle, days after symptom onset, body mass index, and lumbosacral joint angle were the predictors for classifying those injuries. Conclusion The algorithm can be used to identify elementary school-aged children with low back pain requiring advanced imaging investigation, although a future study with a larger sample population is necessary for validating the algorithm.